arXiv subjects
Publications and source records attributed to Akshay Sunil.
Because precipitation is regulated by intricate interactions between topography, cloud microphysics,cumulus convection, radiation, and boundary-layer processes, it is still difficult to accurately simulate orographically enhanced monsoon rainfall over the Western Ghats.
Extreme monsoon rainfall over Kerala presents a major operational forecasting challenge because of the interaction of Arabian Sea moisture transport, mesoscale convection, coastal convergence, and strong orographic forcing along the Western Ghats. Spatial analysis showed that WFS reproduced the principal Western-Ghats-aligned rainfall corridor and represented localized high-intensity rainfall structures more distinctly than the smoother global-model guidance examined for the event. The results demonstrate the complementary roles of operational regional guidance and higher-resolution diagnostic forecasts and highlight the importance of region-specific calibration, spatial verification, and continued evaluation across multiple monsoon events and forecast lead times.
Precipitation nowcasting over the immediate 10-90 min period is important for flood management and real-time decision-making in urban regions. Conventional short-range forecasting with high-resolution numerical weather prediction requires frequent data assimilation, model initialization, and spin-up, introducing computational latency. Machine learning provides an alternative by learning storm evolution directly from high-frequency observations and producing forecasts quickly after training. This is particularly relevant for Mumbai, India, where monsoon convection, land-sea interactions, and localized intense rainfall make short-term prediction difficult. Here, we develop a compact radar-only nowcasting framework that combines multi-elevation reflectivity, Doppler radial velocity, and radial-velocity-gradient proxy features within an encoder-decoder U-Net. Using the most recent radar volume scan, the model predicts 12 future composite reflectivity fields at 7.5-min intervals up to 90 min lead time. The derived velocity magnitude, divergence-like, directional-shear, and vorticity-like channels represent kinematic signatures associated with convergence and boundary interactions without requiring full wind-field retrieval. A high-reflectivity attention module improves sensitivity to convective cores, and physics-guided attribution examines whether the learned sensitivities are meteorologically meaningful. The model is trained using Mumbai Doppler radar observations from May to August 2023 and evaluated on temporally independent events. At 90 min lead time, Critical Success Index values are 0.437, 0.332, and 0.193 for $\geq$10, $\geq$20, and $\geq$30 dBZ thresholds, respectively. Compared with persistence, the model gives lower RMSE and higher spatial correlation at longer lead times. Once trained, it runs on a standard computer, generating nowcasts within seconds for real-time use.
Data driven weather models now approach traditional numerical weather prediction (NWP) skill at short to medium lead times, but their dynamical consistency during autoregressive rollout remains uncertain.
The growing adoption of machine learning (ML) in modelling atmospheric and oceanic processes offers a promising alternative to traditional numerical methods. It is essential to benchmark the performance of both ML and physics-informed ML (PINN) models to evaluate their predictive skill, particularly for short- to medium-term forecasting. In this study, we utilize gridded sea surface temperature (SST) data and six atmospheric predictors (cloud cover, relative humidity, solar radiation, surface pressure, u-component of velocity, and v-component of velocity) to capture both spatial and temporal patterns in SST predictions.
Multi-purpose reservoirs support socioeconomic development by providing irrigation, domestic water supply, hydropower, and other services. However, impoundment of water impacts instream aquatic ecosystems. Thus, the concept of minimum environmental flows (MEFs) was established to restore the benefits of naturally flowing rivers by specifying minimum flow rates to be maintained downstream of dams.But varying legislative contexts under which multi-purpose reservoirs operate may not always necessitate MEF releases. To what extent the release of MEF affects other sectoral benefits remains an open-ended and possibly a site-specific inquiry. A related issue is - how does the order in which releases are prioritized influences sectoral performances? We analyse these issues for the Nagarjuna Sagar reservoir, one of the largest multipurpose reservoirs in southern India. We formulate two versions of a multi-objective decision problem. PF_MEF formulation prioritizes MEF releases over releases for water demand satisfaction, followed by hydropower releases. PF_nMEF formulation follows the regional legislative rule releasing first for demand satisfaction, followed by hydropower and MEF releases. Results thus indicate that prioritizing MEF releases improves can meet MEF requirements without significant compromises in other objectives. We hypothesize that similar investigations may reveal how simple modification of release order may improve ability of other reservoirs to meet environmental goals.
Forecasting rainfall in tropical areas is challenging due to complex atmospheric behaviour, elevated humidity levels, and the common presence of convective rain events. In the Indian context, the difficulty is further exacerbated because of the monsoon intra seasonal oscillations, which introduce significant variability in rainfall patterns over short periods. Earlier investigations into rainfall prediction leveraged numerical weather prediction methods, along with statistical and deep learning approaches. This study introduces deep learning spatial model aimed at enhancing rainfall prediction accuracy on a finer scale. In this study, we hypothesize that integrating physical understanding improves the precipitation prediction skill of deep learning models with high precision for finer spatial scales, such as cities. To test this hypothesis, we introduce a physics informed ConvLSTM2D model to predict precipitation 6hr and 12hr ahead for Mumbai, India. We utilize ERA5 reanalysis data select predictor variables, across various geopotential levels. The ConvLSTM2D model was trained on the target variable precipitation for 4 different grids representing different spatial grid locations of Mumbai. Thus, the use of the ConvLSTM2D model for rainfall prediction, utilizing physics informed data from specific grids with limited spatial information, reflects current advancements in meteorological research that emphasize both efficiency and localized precision.
In this study, the statistical downscaling model (SDSM) is employed for downscaling the precipitation (PREC), maximum temperature (T max ) and minimum temperature (T min ) over Krishna River Basin (KRB). The Canadian Earth System Model, version 2 (CanESM2) General Circulation Model (GCM) outputs were considered as predictor variables. First, the SDSM is calibrated using 30-years (1961-1990) of data and subsequently validated for 15-years (1991-2005). Upon perceiving the satisfactory performance, the SDSM is further used for projecting the predictand variables (PRECP, T max and T min ) for the 21 st century considering three Representative Concentration Pathway (RCP) scenarios viz. RCP2.6, RCP4.5 and RCP8.5. The future period is divided into three 30-year time slices named epoch-1 (2011-2040), epoch-2 (2041-2070) and epoch-3 (2071-2100) respectively. Further, 1976-2005 is considered as baseline period and all the future results are compared with this data. The results were analysed at various temporal scales, i.e., monthly, seasonal and annual. The study reveals that the KRB is going to become wetter during all the seasons. The results are discussed for the worst-case scenario i.e., RCP8.5 epoch-3. The average annual maximum and minimum temperature is expected to increase. The extreme event analysis is also carried out considering the 90 th and 95 th percentile values. It is noticed that the extreme (90 th and 95 th percentiles) are going to increase. There are events more than extreme values. The outcome of this study can be used in flood modelling for the KRB and also for the modelling of future irrigation demands along with the planning of optimal irrigation in the KRB culturable command area.